In [1]:
##BIBLIOTECAS 
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
import yfinance as yf
from binance.client import Client

1) Aquisição dos dados de ações¶

In [2]:
ticket = yf.Ticker("BTC-USD")
#df = ticket.history(period='3y', interval='1mo')
df = ticket.history(interval='1d', start='2018-01-01', end='2024-12-31')
In [3]:
df
Out[3]:
Open High Low Close Volume Dividends Stock Splits
Date
2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0
2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0
2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0
2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0
2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0
... ... ... ... ... ... ... ...
2024-12-26 00:00:00+00:00 99297.695312 99884.570312 95137.882812 95795.515625 47054980873 0.0 0.0
2024-12-27 00:00:00+00:00 95704.976562 97294.843750 93310.742188 94164.859375 52419934565 0.0 0.0
2024-12-28 00:00:00+00:00 94160.187500 95525.898438 94014.289062 95163.929688 24107436185 0.0 0.0
2024-12-29 00:00:00+00:00 95174.054688 95174.875000 92881.789062 93530.226562 29635885267 0.0 0.0
2024-12-30 00:00:00+00:00 93527.195312 94903.320312 91317.132812 92643.210938 56188003691 0.0 0.0

2556 rows × 7 columns

In [4]:
df.tail(15)
Out[4]:
Open High Low Close Volume Dividends Stock Splits
Date
2024-12-16 00:00:00+00:00 104293.578125 107780.578125 103322.984375 106029.718750 91020417816 0.0 0.0
2024-12-17 00:00:00+00:00 106030.687500 108268.445312 105291.734375 106140.601562 68589364868 0.0 0.0
2024-12-18 00:00:00+00:00 106147.296875 106470.609375 100041.539062 100041.539062 93865656139 0.0 0.0
2024-12-19 00:00:00+00:00 100070.687500 102748.148438 95587.679688 97490.953125 97221662392 0.0 0.0
2024-12-20 00:00:00+00:00 97484.695312 98098.914062 92175.179688 97755.929688 105634083408 0.0 0.0
2024-12-21 00:00:00+00:00 97756.195312 99507.101562 96426.523438 97224.726562 51765334294 0.0 0.0
2024-12-22 00:00:00+00:00 97218.320312 97360.265625 94202.187500 95104.937500 43147981314 0.0 0.0
2024-12-23 00:00:00+00:00 95099.390625 96416.210938 92403.132812 94686.242188 65239002919 0.0 0.0
2024-12-24 00:00:00+00:00 94684.343750 99404.062500 93448.015625 98676.093750 47114953674 0.0 0.0
2024-12-25 00:00:00+00:00 98675.914062 99478.750000 97593.468750 99299.195312 33700394629 0.0 0.0
2024-12-26 00:00:00+00:00 99297.695312 99884.570312 95137.882812 95795.515625 47054980873 0.0 0.0
2024-12-27 00:00:00+00:00 95704.976562 97294.843750 93310.742188 94164.859375 52419934565 0.0 0.0
2024-12-28 00:00:00+00:00 94160.187500 95525.898438 94014.289062 95163.929688 24107436185 0.0 0.0
2024-12-29 00:00:00+00:00 95174.054688 95174.875000 92881.789062 93530.226562 29635885267 0.0 0.0
2024-12-30 00:00:00+00:00 93527.195312 94903.320312 91317.132812 92643.210938 56188003691 0.0 0.0

2) Decomposição de uma série temporal¶

Modelo matemático¶

  • Additive Model
    • y(t) = Trend_t + Seasonality_t + Noise_t
  • Multiplicative Model
    • y(t) = Trend_t * Seasonality_t * Noise_t
In [5]:
df[['Close']].head()
Out[5]:
Close
Date
2018-01-01 00:00:00+00:00 13657.200195
2018-01-02 00:00:00+00:00 14982.099609
2018-01-03 00:00:00+00:00 15201.000000
2018-01-04 00:00:00+00:00 15599.200195
2018-01-05 00:00:00+00:00 17429.500000
In [6]:
decomposicao = seasonal_decompose(df[['Close']], model='additive', period=18, extrapolate_trend=30)
In [7]:
df[['Close']].plot()
Out[7]:
<Axes: xlabel='Date'>
No description has been provided for this image
In [8]:
df['Close'].head()
Out[8]:
Date
2018-01-01 00:00:00+00:00    13657.200195
2018-01-02 00:00:00+00:00    14982.099609
2018-01-03 00:00:00+00:00    15201.000000
2018-01-04 00:00:00+00:00    15599.200195
2018-01-05 00:00:00+00:00    17429.500000
Name: Close, dtype: float64
In [9]:
decomposicao.seasonal + decomposicao.resid + decomposicao.trend
Out[9]:
Date
2018-01-01 00:00:00+00:00    13657.200195
2018-01-02 00:00:00+00:00    14982.099609
2018-01-03 00:00:00+00:00    15201.000000
2018-01-04 00:00:00+00:00    15599.200195
2018-01-05 00:00:00+00:00    17429.500000
                                 ...     
2024-12-26 00:00:00+00:00    95795.515625
2024-12-27 00:00:00+00:00    94164.859375
2024-12-28 00:00:00+00:00    95163.929688
2024-12-29 00:00:00+00:00    93530.226562
2024-12-30 00:00:00+00:00    92643.210938
Length: 2556, dtype: float64
In [10]:
decomposicao.trend.iloc[0:5]
Out[10]:
Date
2018-01-01 00:00:00+00:00    15880.219946
2018-01-02 00:00:00+00:00    15684.101005
2018-01-03 00:00:00+00:00    15487.982064
2018-01-04 00:00:00+00:00    15291.863124
2018-01-05 00:00:00+00:00    15095.744183
Name: trend, dtype: float64
In [11]:
decomposicao.plot();
No description has been provided for this image
In [12]:
decomposicao_multi = seasonal_decompose(df[['Close']], model='multiplicative', period=18, extrapolate_trend=30)
In [13]:
decomposicao.seasonal
Out[13]:
Date
2018-01-01 00:00:00+00:00    -10.311643
2018-01-02 00:00:00+00:00    -54.266927
2018-01-03 00:00:00+00:00     72.603848
2018-01-04 00:00:00+00:00    108.665000
2018-01-05 00:00:00+00:00    230.689072
                                ...    
2024-12-26 00:00:00+00:00    -37.067414
2024-12-27 00:00:00+00:00   -114.561807
2024-12-28 00:00:00+00:00    -58.202163
2024-12-29 00:00:00+00:00    -89.561017
2024-12-30 00:00:00+00:00    -31.124074
Name: seasonal, Length: 2556, dtype: float64
In [14]:
decomposicao_multi.seasonal
Out[14]:
Date
2018-01-01 00:00:00+00:00    0.999088
2018-01-02 00:00:00+00:00    0.999050
2018-01-03 00:00:00+00:00    1.002395
2018-01-04 00:00:00+00:00    1.002652
2018-01-05 00:00:00+00:00    1.004031
                               ...   
2024-12-26 00:00:00+00:00    0.997920
2024-12-27 00:00:00+00:00    0.996226
2024-12-28 00:00:00+00:00    0.996047
2024-12-29 00:00:00+00:00    0.994488
2024-12-30 00:00:00+00:00    0.996755
Name: seasonal, Length: 2556, dtype: float64
In [15]:
max(decomposicao_multi.resid)
Out[15]:
1.1935827593006558
In [16]:
ax, fig = plt.subplots(figsize=(15,8))
plt.plot(decomposicao.observed)
plt.plot(decomposicao.trend)
Out[16]:
[<matplotlib.lines.Line2D at 0x14e9e932ed0>]
No description has been provided for this image
In [17]:
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,1, figsize=(12,8))
decomposicao.observed.plot(ax=ax1)
decomposicao.trend.plot(ax=ax2)
decomposicao.seasonal.plot(ax=ax3)
decomposicao.resid.plot(ax=ax4)
plt.tight_layout()
No description has been provided for this image

3) Cálculo da média móvel¶

In [18]:
df ['Close']
Out[18]:
Date
2018-01-01 00:00:00+00:00    13657.200195
2018-01-02 00:00:00+00:00    14982.099609
2018-01-03 00:00:00+00:00    15201.000000
2018-01-04 00:00:00+00:00    15599.200195
2018-01-05 00:00:00+00:00    17429.500000
                                 ...     
2024-12-26 00:00:00+00:00    95795.515625
2024-12-27 00:00:00+00:00    94164.859375
2024-12-28 00:00:00+00:00    95163.929688
2024-12-29 00:00:00+00:00    93530.226562
2024-12-30 00:00:00+00:00    92643.210938
Name: Close, Length: 2556, dtype: float64
In [19]:
df['Close'].rolling(7).mean()
Out[19]:
Date
2018-01-01 00:00:00+00:00             NaN
2018-01-02 00:00:00+00:00             NaN
2018-01-03 00:00:00+00:00             NaN
2018-01-04 00:00:00+00:00             NaN
2018-01-05 00:00:00+00:00             NaN
                                 ...     
2024-12-26 00:00:00+00:00    96934.662946
2024-12-27 00:00:00+00:00    96421.652902
2024-12-28 00:00:00+00:00    96127.253348
2024-12-29 00:00:00+00:00    95902.294643
2024-12-30 00:00:00+00:00    95610.433036
Name: Close, Length: 2556, dtype: float64
In [20]:
media_movel7d = df['Close'].rolling(7).mean()
media_movel14d = df['Close'].rolling(14).mean()
media_movel21d = df['Close'].rolling(21).mean()
In [21]:
fig, ax = plt.subplots(figsize=(14,5))
plt.plot(media_movel7d, 'orange')
plt.plot(media_movel14d, 'red')
plt.plot(media_movel21d, 'black')
plt.plot(df['Close'])
Out[21]:
[<matplotlib.lines.Line2D at 0x14e9ffcb190>]
No description has been provided for this image

4) Extração de features¶

In [22]:
df.head()
Out[22]:
Open High Low Close Volume Dividends Stock Splits
Date
2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0
2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0
2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0
2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0
2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0
In [23]:
df.reset_index(inplace=True)
In [24]:
df.head()
Out[24]:
Date Open High Low Close Volume Dividends Stock Splits
0 2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0
1 2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0
2 2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0
3 2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0
4 2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0
In [25]:
# criar features para cada período
df['year'] = df['Date'].dt.year
df['month'] = df['Date'].dt.month
df['day'] = df['Date'].dt.day
In [26]:
df.head()
Out[26]:
Date Open High Low Close Volume Dividends Stock Splits year month day
0 2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0 2018 1 1
1 2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0 2018 1 2
2 2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0 2018 1 3
3 2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0 2018 1 4
4 2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0 2018 1 5
In [27]:
# Cálculo da rentabilidade: preço atual/ preço anterior * 100 - 100
df[['Close']]
Out[27]:
Close
0 13657.200195
1 14982.099609
2 15201.000000
3 15599.200195
4 17429.500000
... ...
2551 95795.515625
2552 94164.859375
2553 95163.929688
2554 93530.226562
2555 92643.210938

2556 rows × 1 columns

In [28]:
df['Close'].head()
Out[28]:
0    13657.200195
1    14982.099609
2    15201.000000
3    15599.200195
4    17429.500000
Name: Close, dtype: float64
In [29]:
df['Close'].shift()
Out[29]:
0                NaN
1       13657.200195
2       14982.099609
3       15201.000000
4       15599.200195
            ...     
2551    99299.195312
2552    95795.515625
2553    94164.859375
2554    95163.929688
2555    93530.226562
Name: Close, Length: 2556, dtype: float64
In [30]:
df['rentabilidade'] = df['Close'] / df['Close'].shift() * 100 - 100
In [31]:
df.head()
Out[31]:
Date Open High Low Close Volume Dividends Stock Splits year month day rentabilidade
0 2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0 2018 1 1 NaN
1 2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0 2018 1 2 9.701106
2 2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0 2018 1 3 1.461080
3 2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0 2018 1 4 2.619566
4 2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0 2018 1 5 11.733293
In [32]:
def features_extraction(df_):
    #criar features para cada período de forma diferente
    df_['year'] = df_['Date'].dt.year
    df_['month'] = df_['Date'].dt.month
    df_['day'] = df_['Date'].dt.day
    df_['rentabilidade'] = df_['Close'] / df_['Close'].shift() * 100 - 100
In [33]:
df.reset_index(inplace=True)
In [34]:
features_extraction(df)
In [35]:
df.head()
Out[35]:
index Date Open High Low Close Volume Dividends Stock Splits year month day rentabilidade
0 0 2018-01-01 00:00:00+00:00 14112.200195 14112.200195 13154.700195 13657.200195 10291200000 0.0 0.0 2018 1 1 NaN
1 1 2018-01-02 00:00:00+00:00 13625.000000 15444.599609 13163.599609 14982.099609 16846600192 0.0 0.0 2018 1 2 9.701106
2 2 2018-01-03 00:00:00+00:00 14978.200195 15572.799805 14844.500000 15201.000000 16871900160 0.0 0.0 2018 1 3 1.461080
3 3 2018-01-04 00:00:00+00:00 15270.700195 15739.700195 14522.200195 15599.200195 21783199744 0.0 0.0 2018 1 4 2.619566
4 4 2018-01-05 00:00:00+00:00 15477.200195 17705.199219 15202.799805 17429.500000 23840899072 0.0 0.0 2018 1 5 11.733293

Hipotese: 1) Existe um melhor mês para investir no ativo?¶

Verificar o mês com menor rentabilidade, ou seja, período de baixa (nos últimos 7 anos):¶

In [36]:
df.groupby('month').agg({'rentabilidade':'sum'}).plot(kind='bar')
Out[36]:
<Axes: xlabel='month'>
No description has been provided for this image
In [37]:
df.set_index('Date', inplace=True)
In [38]:
media_movel30d = df['Close'].rolling(30).mean()
media_movel90d = df['Close'].rolling(90).mean()
fig, ax = plt.subplots(figsize=(8,4))
plt.plot(df['Close'])
plt.plot(media_movel30d, 'orange')
plt.plot(media_movel90d, 'green')
Out[38]:
[<matplotlib.lines.Line2D at 0x14e9e7c7d10>]
No description has been provided for this image

Hipotese: 2) Existe um melhor dia para investir no BTC?¶

In [39]:
df.groupby('day').agg({'rentabilidade':'sum'}).plot(kind='bar')
Out[39]:
<Axes: xlabel='day'>
No description has been provided for this image
OBS: Quanto menor a sua rentabilidade, melhor oportunidade de compra.¶
In [40]:
df.iloc[0]
Out[40]:
index            0.000000e+00
Open             1.411220e+04
High             1.411220e+04
Low              1.315470e+04
Close            1.365720e+04
Volume           1.029120e+10
Dividends        0.000000e+00
Stock Splits     0.000000e+00
year             2.018000e+03
month            1.000000e+00
day              1.000000e+00
rentabilidade             NaN
Name: 2018-01-01 00:00:00+00:00, dtype: float64

5) Correlação de séries temporais¶

In [41]:
tickets = ['ETH-USD', 'SOL-USD', 'BNB-USD', 'XRP-USD', 'ADA-USD', 'USDBRL=X']
In [42]:
dfs = []

for t in tickets:
    print('Reading ticker {}...' .format(t))
    ticket = yf.Ticker(t)
    aux = ticket.history(interval='1d', start='2021-01-01', end='2024-12-31')
    aux.reset_index(inplace=True)
    aux['ticket'] = t
    dfs.append(aux)

Reading ticker ETH-USD...
Reading ticker SOL-USD...
Reading ticker BNB-USD...
Reading ticker XRP-USD...
Reading ticker ADA-USD...
Reading ticker USDBRL=X...
In [43]:
dfs[1]
Out[43]:
Date Open High Low Close Volume Dividends Stock Splits ticket
0 2021-01-01 00:00:00+00:00 1.509775 1.859656 1.502038 1.842084 25722549 0.0 0.0 SOL-USD
1 2021-01-02 00:00:00+00:00 1.845586 1.989295 1.721482 1.799275 31671064 0.0 0.0 SOL-USD
2 2021-01-03 00:00:00+00:00 1.799902 2.364981 1.799902 2.161752 55073422 0.0 0.0 SOL-USD
3 2021-01-04 00:00:00+00:00 2.162412 2.485097 1.876342 2.485097 59955405 0.0 0.0 SOL-USD
4 2021-01-05 00:00:00+00:00 2.490982 2.502616 2.077742 2.157217 50555207 0.0 0.0 SOL-USD
... ... ... ... ... ... ... ... ... ...
1455 2024-12-26 00:00:00+00:00 197.475159 199.397980 186.855591 188.218109 2864701219 0.0 0.0 SOL-USD
1456 2024-12-27 00:00:00+00:00 188.216553 193.753357 182.947128 183.828476 2788285851 0.0 0.0 SOL-USD
1457 2024-12-28 00:00:00+00:00 183.833389 195.435440 183.833328 195.013535 2158687217 0.0 0.0 SOL-USD
1458 2024-12-29 00:00:00+00:00 195.022446 197.181076 188.341003 189.744263 2165753102 0.0 0.0 SOL-USD
1459 2024-12-30 00:00:00+00:00 189.744202 196.122910 185.937607 191.028046 3246300054 0.0 0.0 SOL-USD

1460 rows × 9 columns

In [44]:
for d in dfs:
    features_extraction(d)
In [45]:
dfs[0]
Out[45]:
Date Open High Low Close Volume Dividends Stock Splits ticket year month day rentabilidade
0 2021-01-01 00:00:00+00:00 737.708374 749.201843 719.792236 730.367554 13652004358 0.0 0.0 ETH-USD 2021 1 1 NaN
1 2021-01-02 00:00:00+00:00 730.402649 786.798462 718.109497 774.534973 19740771179 0.0 0.0 ETH-USD 2021 1 2 6.047287
2 2021-01-03 00:00:00+00:00 774.511841 1006.565002 771.561646 975.507690 45200463368 0.0 0.0 ETH-USD 2021 1 3 25.947533
3 2021-01-04 00:00:00+00:00 977.058838 1153.189209 912.305359 1040.233032 56945985763 0.0 0.0 ETH-USD 2021 1 4 6.635042
4 2021-01-05 00:00:00+00:00 1041.498779 1129.371460 986.811279 1100.006104 41535932781 0.0 0.0 ETH-USD 2021 1 5 5.746123
... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 2024-12-26 00:00:00+00:00 3493.304199 3512.604492 3302.306396 3331.225830 22247726776 0.0 0.0 ETH-USD 2024 12 26 -4.637807
1456 2024-12-27 00:00:00+00:00 3331.053711 3436.710693 3302.575684 3328.916992 24091627403 0.0 0.0 ETH-USD 2024 12 27 -0.069309
1457 2024-12-28 00:00:00+00:00 3328.774658 3419.920166 3318.033936 3397.902344 14305648523 0.0 0.0 ETH-USD 2024 12 28 2.072306
1458 2024-12-29 00:00:00+00:00 3397.862549 3406.648438 3321.664795 3349.513428 13440907792 0.0 0.0 ETH-USD 2024 12 29 -1.424082
1459 2024-12-30 00:00:00+00:00 3349.585938 3428.527344 3298.804443 3356.392578 26981583962 0.0 0.0 ETH-USD 2024 12 30 0.205378

1460 rows × 13 columns

In [46]:
correlacao = pd.DataFrame()
for d in dfs:
    correlacao[d['ticket'].iloc[0]] = d['rentabilidade']
In [47]:
correlacao.head()
Out[47]:
ETH-USD SOL-USD BNB-USD XRP-USD ADA-USD USDBRL=X
0 NaN NaN NaN NaN NaN NaN
1 6.047287 -2.323944 0.887962 -6.649569 1.182210 -0.003857
2 25.947533 20.145722 7.602682 1.891682 15.540266 1.991568
3 6.635042 14.957541 -0.541024 4.794818 9.642667 -0.211507
4 5.746123 -13.193848 1.974881 -4.194326 14.927800 0.545032
In [48]:
correlacao.corr()
Out[48]:
ETH-USD SOL-USD BNB-USD XRP-USD ADA-USD USDBRL=X
ETH-USD 1.000000 0.625088 0.639170 0.543584 0.673762 -0.015829
SOL-USD 0.625088 1.000000 0.546888 0.442368 0.545256 -0.028577
BNB-USD 0.639170 0.546888 1.000000 0.469745 0.550990 -0.002799
XRP-USD 0.543584 0.442368 0.469745 1.000000 0.568396 0.019685
ADA-USD 0.673762 0.545256 0.550990 0.568396 1.000000 0.000785
USDBRL=X -0.015829 -0.028577 -0.002799 0.019685 0.000785 1.000000
OBS: Quanto mais próximos de 1, mais correlacionados são os ativos.¶

Visualização de dados usando Seaborn¶

In [49]:
import seaborn as sns
In [50]:
ax, fig = plt.subplots(figsize=(20,5))
ax = sns.heatmap(correlacao.corr(), annot=True)
No description has been provided for this image

Visualização de dados usando Plotly¶

In [51]:
import plotly.graph_objs as go
import plotly.io as pio
pio.renderers.default = 'notebook'
In [52]:
def plot_lines(df_, columns=['Open', 'Close', 'High', 'Low']):

    fig = go.Figure()
    for c in columns:
        fig.add_trace(go.Scatter(x = list(df_.index),
                            y = df_[c],
                            mode = 'markers+lines',
                            name = c))
        fig.show()
In [53]:
plot_lines(df)
In [54]:
def plotCandleStick(df, acao='tickets'):
    tracel = {
        'x': df.index,
        'open': df.Open,
        'close': df.Close,
        'high': df.High,
        'low': df.Low,
        'type': 'candlestick',
        'name': acao,
        'showlegend': False
    }

    data = [tracel]
    layout = go.Layout()

    fig = go.Figure(data=data, layout=layout)
    fig.show()
In [55]:
plotCandleStick(df)
In [ ]: